The Feature Extraction Method of EEG Signals Based on Transition Network

• Published in: Advances in Neural Networks - ISNN 2017 Vol. 10262; pp. 491 - 497
• Main Authors: Liu, Mingmin, Meng, Qingfang, Zhang, Qiang, Wang, Dong, Zhang, Hanyong
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Epilepsy EEG automatic detection; Transition network; Variance of degree sequence
• ISBN: 9783319590806; 3319590804
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-59081-3_57

High accuracy of epilepsy EEG automatic detection has important clinical research significance. The combination of nonlinear time series analysis and complex network theory made it possible to analyze time series by the statistical characteristics of complex network. In this paper, based on the transition network the feature extraction method of EEG signals was proposed. Based on the complex network, the epileptic EEG data were transformed into the transition network, and the variance of degree sequence was extracted as the feature to classify the epileptic EEG signals. Experimental results show that the single feature classification based on the extracted feature obtains classification accuracy up to 98.5%, which indicates that the classification accuracy of the single feature based on the transition network was very high.